Locating external interference in a wireless network

ABSTRACT

A method for localizing interference uses data available to wireless communication networks to determine probabilities of a source of external interference being located at a plurality of predetermined locations. In a heterogeneous network, data from sites using omnidirectional antennas can be combined with data from multi-sector sites to accurately locate a source of interference.

CROSS-REFERENCES TO RELATED APPLICATIONS

This present application is a continuation-in-part of U.S. patentapplication Ser. No. 16/211,181, filed Dec. 5, 2018. This presentapplication also claims priority to U.S. Provisional Application No.62/839,730, filed Apr. 28, 2019, which is incorporated by referenceherein.

BACKGROUND

The wide adoption of mobile devices along with ubiquitous cellular datacoverage has resulted in an explosive growth of mobile applications thatexpect always-accessible wireless networking. This growth has placeddemands on network performance including demands for fast and reliablecommunication paths, which causes increasing strain on the limitedradio-frequency (RF) spectrum allocated to wireless telecommunication.Accordingly, efficient use of the limited spectrum is increasinglyimportant to the advancement of wireless communication technology.

Interference is a barrier to efficient use of wireless spectrum. Modemwireless communications operate in interference limited environmentswhere signal quality to and from network subscriber devices is limitedin part by interference from other users served by the same or nearbycells. The design and optimization of these networks are based on havingclear spectrum occupied only by radio frequency transmitting equipmentassociated with the specific network. However, this ideal of clearspectrum occupied only by intended users of the system is not alwaysachieved.

Real world systems often experience unexpected network interferencewhich may originate from radio frequency (RF) generating sources thatare not otherwise associated with a licensed wireless network. Thesepotential interference sources include many things such as industrialmachinery, electronics test equipment radiating signals in the bands ofinterest, undesired mixing products generated by the licensed systemitself and illegal radio sources. The result of these systeminterference sources is degraded system service and reduced wirelessnetwork capacity and coverage as the intentional signals suffer capacityand quality losses due to these interferers.

After determining that external interference is present, geo-locationtechniques can be used to characterize and locate a source of theinterference. Another common application for geo-location techniques isto locate a 911 caller.

Various geo-location techniques, such as, Time-of-Arrival (TOA) andTime-Difference-of-Arrival (TDOA) techniques, Power-of-Arrival (POA) andPower-Difference-of-Arrival (PDOA) techniques,Frequency-Difference-of-Arrival (FDOA) techniques, and Angle-of-Arrival(AoA) geolocation techniques have been widely used to detect and locateradio signal emitters. However, measured time or frequency-basedtechniques require a precise timing source such as a common clock or acommon frequency reference, which is not typically present for externalinterference sources.

POA or PDOA can be used to detect and locate an external interferencesource by collecting power measurement data from three or more receiverswith known locations. However, the accuracy of conventional measuredpower-based techniques is highly limited by fading and shadowing in theradio environment, and may require additional measurement systems tocollect the measured power data in order to make an accurate locationdetermination.

TECHNICAL FIELD

Embodiments of the present disclosure relate to a system and method thatdetects and locates interference sources in a wireless communicationnetwork using data collected by the network.

BRIEF SUMMARY

The present disclosure describes a method and system that generates andrefines a probability map for identifying a source of interference. Inan embodiment, an interference search area is created using a Bayesianapproach, and the search are is refined using a contour map createdusing data from omni-directional cell sites. In another embodiment, theinterference search area is improved using coverage areas formulti-sector sites.

In an embodiment of the present disclosure, a method for locating asource of interference affecting a wireless telecommunications networkincludes generating a first probability map that indicates probabilitiesof the source of interference being disposed in respective geographiclocations, receiving interference data for at least one omnidirectionalantenna that is disposed in a location covered by the map, generating acontour map of interference levels using the received interference data,and generating a second probability map by re-assigning probabilityvalues of the first probability map according to contours of the contourmap. The first probability map may have an unbounded probability areaoriginating from a directional antenna location, and re-assigning theprobability values of the first map may include bounding the unboundedprobability area.

In an embodiment, the first probability map includes a second unboundedprobability area, and re-assigning the probability values includesreducing probability values of the second unbounded probability area tozero. The first probability map may have a plurality of pixels, whereeach pixel has an assigned probability value that represents aprobability that the source of interference is located in a geographicarea corresponding to the pixel. The method may include comparingcharacteristics of interference used to generate the first probabilitymap to characteristics of the received interference data from the atleast one omnidirectional antenna to determine whether the interferenceused to generate the first probability map has the same characteristicsas interference associated with the received interference data.

In an embodiment, generating the contour map includes generating aplurality of bounded shapes, each shape having a perimeter line thatrepresents a particular level of interference. The method may includeassigning an attenuation value to each bounded shape, wherein generatingthe second probability map includes re-assigning probability values ofthe first map according to the attenuation values of the bounded shapes.

In an embodiment, generating the contour map includes performingDelaunay triangulation to connect locations for the omnidirectionalantennas, and the contours include contour lines between sides of theDelaunay triangles. At least one of the locations for theomnidirectional antennas used to generate the contour map may be avirtual cell site at which no physical cell site exists, and theinterference may be external interference that is not caused by thewireless telecommunications network. The method may further includeresolving the external interference by performing physical activities,including making physical changes to the network configuration. In someembodiments, the method described above is performed by a spectrumanalysis server comprising a processor and a non-transitory computerreadable medium with instructions stored thereon which, when executed bythe processor, perform the steps described above.

This disclosure describes a system and method that use networkmanagement data to locate external interference. The network managementdata may include Configuration Management (CM) data, PerformanceManagement (PM) data and topology data. Identifying the location ofexternal interference source is accomplished by generating a probabilityheat map of possible locations of external interference around theaffected area. Embodiments can determine location with a high degree ofaccuracy in a complex radio environment.

In an embodiment, a method for locating a source of interferenceexternal to a wireless telecommunications network includes receivingsignal strength measurement data from a plurality of cellular antennasof multi-sector cell sites, determining that an external interferencesignal is present in the measurement data, establishing a grid of pixelsthat represents an area associated with the plurality of cellularantennas, determining respective first signal strength values for thepixels in the grid, and determining respective probability values forthe pixels in the grid by comparing the signal strength measurement datato the first signal strength values.

In an embodiment, determining the first signal strength values includesdetermining an angle of arrival for a pair of antennas of a cell sitefor each of the pixels and determining an expected interference powerfor each of the pixels using the associated angles of arrival.Determining the respective probability values may include determiningdifferences between the measured values for antennas in an antenna pairand a hypothetical interference value for each pixel, and assigningrespective probability values to each pixel based on the differences.

In an embodiment, when a measured value of a first antenna of theantenna pair is equal to the noise floor and a measured value of asecond antenna of the antenna pair is above the noise floor, the firstsignal strength value of the second antenna is set based on a differencebetween the measured value of the second antenna and the noise floor.

In an embodiment, assigning the probability values includes segmenting anormal distribution into a set of binned values and assigning one thebinned values as the probability value for each pixel for a respectiveantenna pair. The probability values may be assigned by combining thebinned values of antenna pairs of each cell site and combiningprobability values of each cell site, wherein the respective probabilityvalues for the pixels are the combined probability values of each cellsite.

In an embodiment, the grid comprises a plurality of shapes thatrepresent the pixels, and the grid is associated with a geographic areathat includes all of the multi-sector cell sites.

Determining the respective probability values for pixels may includedetermining at least two local probability maxima within the grid, eachof the local probability maxima being associated with a respectivesource of the external interference. An embodiment may include providinga heat map indicating probability values of at least a portion of thepixels in the grid.

Comparing the signal strength measurement data to the first signalstrength values may include determining differences between the signalstrength measurement data and the first signal strength values todetermine the respective probability values.

Embodiments of the present application include a wirelesstelecommunications system comprising a spectrum analysis serverconfigured to locate a source of interference external to a wirelesstelecommunications network, the spectrum analysis server comprising aprocessor and a non-transitory computer readable medium withinstructions stored thereon which, when executed by the processor,perform one or more of the steps provided above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for identifying interference in a cellularcommunications network according to an embodiment.

FIG. 2 illustrates a network device according to an embodiment.

FIG. 3 illustrates an embodiment of a process for determining a locationof an external source of interference at a cell site.

FIG. 4 illustrates an embodiment of a grid pattern around cells ofinterest.

FIG. 5 illustrates an embodiment of a Bayesian process for determiningprobabilities.

FIG. 6A illustrates angles of arrival for antennas of a three-sectorcell site, and FIG. 6B illustrates antenna gain for the three-sectorcell site.

FIG. 7 illustrates a segmented normal distribution.

FIG. 8A illustrates pixel probabilities for a single cell site, FIG. 8Billustrates pixel probabilities for two cell sites, and FIG. 8Cillustrates pixel probabilities for three cell sites.

FIG. 9A illustrates a process for determining a range of an angle ofarrival for a pixel, FIG. 9B illustrates pixel shapes circumscribed bycircles, and FIG. 9C illustrates a cell site in relation to acircumscribed pixel.

FIG. 10 illustrates a process for determining probabilities usingEuclidian distance.

FIG. 11 illustrates an embodiment of a heat map showing a distributionof probability values in a grid of pixels.

FIG. 12 illustrates an embodiment of a heat map created using the samedata as FIG. 11 that is scaled to show local maxima and a relativelyminor variance of probability values for surrounding pixels.

FIG. 13 illustrates a heat map resulting from a trilateration process.

FIG. 14 illustrates a process that uses data from omnidirectionalantennas to locate a source of interference.

FIG. 15 illustrates a process for creating a signal strength contourmap.

FIG. 16 illustrates interference measurements for omnidirectionalantennas.

FIG. 17 illustrates a Delaunay triangulation of the omnidirectionalantenna measurements.

FIG. 18 illustrates a contour map of interference levels.

FIG. 19 illustrates an open-sided contour map of interference levels.

FIG. 20 illustrates a contour map that uses a virtual cell site to closethe contours.

FIG. 21A is a probability map, FIG. 21B is a contour map, and FIG. 21Cis a result of combining the probability map with the contour map.

FIG. 22A is a probability map, FIG. 22B is a contour map, and FIG. 22Cis a result of combining the probability map with the contour map.

FIG. 23A is a probability map, FIG. 23B is a map of coverage estimates,and FIG. 23C is a result of combining the probability map with the mapof coverage estimates.

DETAILED DESCRIPTION

A detailed description of embodiments is provided below along withaccompanying figures. The scope of this disclosure is limited only bythe claims and encompasses numerous alternatives, modifications andequivalents. Although steps of various processes are presented in aparticular order, embodiments are not necessarily limited to beingperformed in the listed order. In some embodiments, certain operationsmay be performed simultaneously, in an order other than the describedorder, or not performed at all.

Numerous specific details are set forth in the following description inorder to provide a thorough understanding. These details are providedfor the purpose of example and embodiments may be practiced according tothe claims without some or all of these specific details. For the sakeof clarity, technical material that is known in the technical fieldsrelated to this disclosure has not been described in detail so that thedisclosure is not unnecessarily obscured.

FIG. 1 illustrates a networked spectrum analysis system 100 according toan embodiment. The system 100 integrates information from availablewireless network sources to detect, isolate, characterize and locateundesired radio frequency interference in the context of a wirelessnetwork. Sources of this information, which are hardware elements of awireless network, are available in typical wireless cellular networks,but they are not conventionally connected and configured in the mannersuggested by this disclosure. In particular, the spectrum analyticsserver 140 is a novel component of a telecommunications network.

A radio access portion of system 100 may include one or more basestations 102, each of which are equipped with one or more antennas 104.Each of the antennas 104 provides wireless communication for userequipment 108 in one or more cells 106. As used herein, the term “basestation” refers to a wireless communications station that serves as ahub of a wireless network. For example, in a Long Term Evolution (LTE)cellular network, a base station may be an eNodeB.

The base stations 102 may provide service for macrocells, microcells,picocells, or femtocells 106. FIG. 1 shows an embodiment in which basestation 102 provides wireless communication services to three cells 106.The cells may be specific to a particular Radio Access Technology (RAT)such as GSM, UMTS, LTE, NR, etc.

Due to the directionality of some RF antennas 104, each base station 102may serve a plurality of cells 106 arrayed about the base station site.In a typical deployment, a base station 102 provides three to six cells106, which are deployed in a sectorized fashion at a site. In otherembodiments, one or more base station 102 may be outfitted with anomnidirectional antenna that provides service to a single cell for agiven RAT.

Multiple base stations 102 may be present at a site and each basestation may support one or more cellular communications technologies(e.g., a base station may support UMTS and LTE cells). The one or moreUE 108 may include cell phone devices, laptop computers, handheld gamingunits, electronic book devices and tablet PCs, and any other type ofcommon portable or fixed wireless computing device that are providedwith wireless communications services by a base station 102.

The system 100 may include a backhaul portion 110 that can facilitatedistributed network communications between core elements 112, 114 and116 and one or more base station 102 within a cellular network. In anembodiment, the backhaul portion of the network includes intermediatelinks between a backbone of the network which is generally wire line,and sub-networks or base stations 102 located at the periphery of thenetwork. The network connection between any of the base stations 102 andthe rest of the world may initiate with a link to the backhaul portionof a provider's communications network. A backhaul 110 may include an X2connection through which base stations 102 communicate with one anotherdirectly.

The core network devices 112, 114 and 116 may be any of a plurality ofnetwork equipment such as a Radio Resource Manager (RRM), a MobilityManagement Entity (MME), a serving gateway (S-GW), a Radio NetworkController (RNC), a base station controller (BSC), a mobile switchingcenter (MSC), a Self-Organizing Network (SON) server, an Evolved ServingMobile Location Server (eSMLC), a Home Subscriber Server (HSS), etc.Persons of skill in the art will recognize that core network devices112, 114 and 116 are different depending on the particular RAT or set ofRATs that are present in the network. The core network devices support aradio access portion of the network that includes the base stations 102.

Elements of the communications network 100 are part of an ElementManagement System (EMS) 120 and a Performance Monitoring (PM) system122. The PM system 122 may include base stations 102 as well as corenetwork equipment that collect and process performance data andperformance metrics for the network. A spectrum analysis server 140interfaces with various network components, including components of thePM system 122 and the EMS 120.

Although FIG. 1 shows the spectrum analysis server as a single, discretecomponent, embodiments are not so limited. For example, in otherembodiments, components of the spectrum analysis server 140 may bedistributed among multiple computing entities. In addition, hardware forthe spectrum analysis server may perform processes not directly relatedto interference.

The performance monitoring system 122 generates performance data 126 forthe wireless network. The PM data 126 may be derived from observationsof network performance, which may be reported at a predetermined timeinterval, e.g., every minute, 5 minutes, 15 minutes, hourly or daily. PMdata 126 may include raw event counts (e.g. counts of dropped calls orhandover failures during the observation period) or complex derivedperformance indicators (e.g. noise rise normalized by user loading,Channel Quality Indicator (CQI) distribution statistics normalized bydata volume, etc.). PM data 126 may include raw or aggregatedperformance data.

In some embodiments, PM data 126 includes data input from a dedicated PMtool, as well as data received directly from EMS 120, or elements of theOperations and Support System (OSS). In an embodiment, PM data 126 maybe derived directly from network event data by the spectrum analyticsserver 140. For example, in an embodiment, when event data 136 isavailable to the spectrum analytics server 140, the server may aggregateindividual events to create equivalent PM counters and Key PerformanceIndicators (KPIs). Thus, in some embodiments, PM data 126 is derivedfrom sources other than a PM system 122.

Fault Management Data 128 may be transmitted from the PM system 122 tospectrum analysis server 140. Fault Management Data 128 includes, forexample, alarm data that indicates performance issues at one or morecell site.

Configuration Management (CM) data 130 is input to the spectrum analysisserver 140 from EMS 120. CM data 130 includes the current configurationof various wireless network equipment, such as the configuration of basestations 102 and core components such as Radio Network Controllers.

CM Data 130 is quasi-static and typically only changes as a result ofdeploying new network equipment, network optimization such as cellsplitting, cell ID reassignment, changes in operating frequency ortransmit power, etc. CM data 130 may include pertinent information suchas cell technology (e.g., 2G GSM, 3G UMTS, 4G LTE, 5G NR) associatedwith physical and logical network elements, operating frequency,transmit power, reuse codes, type of cell (e.g. macro, micro, picocell), and other information related to the configuration of the radionetwork elements.

Topology data 132 is data relating to the location and orientation ofnetwork elements, including information such as the antenna latitude andlongitude of a base station 102, antenna height, pointing angle forsectorized antennas, antenna beamwidth, site deployment type (e.g.indoor, outdoor, distributed antenna system, etc.), etc. In addition tointerference detection and characterization, topology data 132 may beused to aid in correlating PM data 126 and network event data 136against actual physical locations, and for understanding physicaldistance and orientation relationships between network elements.

RF planning tool 124 has network planning information used to determinecell site positions and pertinent parameters such as sector pointingangles. Network parameters established via automated or manual networkplanning processes may be used to configure the actual network equipmentand serve as source information for some of the CM data 130 and Topologydata 132. Alternative embodiments may include a direct data connectionbetween entities that perform RF planning functions and the spectrumanalysis server 140, provided that the network CM data 130 and topologydata 132 is available to support processes described in this disclosure.

Network event data 136 represents discrete network events that aretypically logged by network elements. Network event data 136 may includeinformation pertaining to the start and termination of phone calls,information regarding handover of UEs 108 between network cells 106,measurement reports sent by UEs to network elements, as well as periodicreporting at intervals of as low as several seconds or less betweenreporting periods. Network event data 136 may be available via acontinuous streaming mechanism, or recorded and stored in files atnetwork elements that contain, for example, fifteen to thirty minutes ormore of network event data. Because event data 136 is typicallytimestamped with sub-second resolution, it can be used to determinevariance of conditions over time at relatively short intervals, such asfive minutes, one minute, 30 seconds, or as low as the reportinginterval, which may be less than one second.

Network event data 136 may include call event data, or cell trace dataaccording to LTE terminology. Call trace data includes informationidentifying makes and models of UEs 108, and is typically used byoperators to determine device-specific network faults, e.g. that aparticular cell phone model has an unusual rate of handover failuresunder certain conditions. Examples of call event data 136 includetracking area messages, request for retries, RSSI measurements, andprotocol messages. Network event data 136 is not conventionally used forinterference detection, characterization or identifying location.

Tools supporting the collection of network event 136 data may beconfigured to collect selected event types, or to subsample themessaging to a subset of active users. Smaller size network event filesare useful in measuring implied loading on network data transport suchas wireless base station backhaul. When properly configured, networkevents provide high resolution and near real-time information regardingthe operation of targeted network base stations 102, which can be usedas part of the interference detection processes described by thisdisclosure.

The collection point for network event data 136 varies between specificwireless technologies and may vary in vendor-specific implementations.For instance, network event data 136 is typically collected at the RNCentity in 3GPP defined 3G networks (i.e., UMTS, HSPA), but network eventdata 136 is collected by the eNodeB entity in 4G LTE systems. Networkevent recordings may be pulled directly from the network elements thatstore the events by the spectrum analysis server 140, or automaticallystored on a separate data storage server, or staging server, such thatexternal systems such as the spectrum analytics server 140 may accessnetwork event data 136 without incurring additional data loading on thenetwork elements. Accordingly, it should be understood that networkevent data 136 may be collected, stored and retrieved in various ways indifferent embodiments.

The network event data 136 may be collected by a trace utility 134 thatis integrated with a cellular network. Trace concepts and requirementsare explained, for example, in the Third Generation Partnership Project(3GPP) Technical Specification TS 32.421.

An embodiment may use network event data 136. In such an embodiment,spectrum analysis may derive base station performance indicatorsdirectly from network event data 136 in conjunction with, or in place ofinputs from a Performance Monitoring system 122. In such an embodiment,network event data records may be aggregated.

Embodiments of this disclosure may utilize additional informationsources beyond the sources illustrated in FIG. 1, such as informationprovided by SON (Self Organizing Network) tools, including analysis andinsight into neighbor relationships not readily apparent from thesources listed above. Additional external integrations may also includeradio frequency propagation planning tools that may be used to enhanceaccuracy of interference detection and interference localization.

Also shown in FIG. 1 is a plurality of energy sensing probes 118, whichmay be dedicated to the task of sensing energy in particular frequenciesand reporting energy detection results. Energy sensing probes 118 may beinstalled at various locations in a network, and may be tuned to detectenergy on one or more frequencies that are used for wirelesscommunications. Although embodiments of this disclosure do not requirethe deployment of dedicated energy sensing probes 118, their use is notprecluded.

In an embodiment that uses dedicated energy detection probes 118, energymeasurements derived directly from the detection probes are used inconjunction with or in place of energy measurements derived from networkevent data 136 as inputs to the spectrum analytics server 140.Subsequently, the measurements from dedicated energy detection probes118 may be correlated with known probe positions and probe configurationcharacteristics (e.g. directional antennas) as well as current networkloading data to enhance the detection of undesired radio frequencyinterference within the network.

Probe data may in some cases be broadband and cover wider spectrumsegments than the operational wireless network, and as such may be usedto monitor bands adjacent to or even highly separated from currentwireless bands. Such broadband spectrum analytics sensing may be used tosupport frequency agile spectrum sharing solutions whereby internal bandmetrics, e.g. network event data correlated with topology and CM data,as well as probe supported broad band metrics, to enhance performance ofspectrum analytics.

The spectrum analysis server 140 represents a specific processing devicethat interfaces with one or more of the external data sources describedabove. The spectrum analysis server 140 may perform one or more ofanomaly and interference detection, analysis, comparison,characterization and localization processes described in thisdisclosure. In an embodiment, the spectrum analysis server 140 isphysically located in an operator's Network Operations Center (NOC).From a logical perspective, the spectrum analysis server 140 is locatedin the Operations Support System (OSS) plane. The spectrum analysisserver 140 may perform one or more of the specific processing stepsdescribed below.

FIG. 2 illustrates a block diagram of a network device 200 that mayrepresent UE 108, network controller devices 110, 112 and 114, aspectrum analysis server 140, etc. The network device 200 has one ormore processor devices including a CPU 204. The CPU 204 is responsiblefor executing computer programs stored on volatile (RAM) and nonvolatile(ROM) memories 202 and a storage device 212 (e.g., HDD or SSD). In someembodiments, storage device 212 may store program instructions as logichardware such as an ASIC or FPGA. The storage device 212 and ROM ofmemory 202 are non-transitory computer readable media that may havecomputer executable instructions stored thereon which, when executed bythe CPU 204, cause the network device to perform one or more operationsaccording to embodiments of the present disclosure.

The network device 200 may also include a user interface 206 that allowsa user to interact with the network device's software and hardwareresources and to display the performance and operation of the system100. In addition, the network device 200 may include a network interface206 for communicating with external devices, and a system bus 210 thatfacilitates data communications between the hardware resources of thenetwork device. If the Network device 200 has wireless connectivity, itmay have a transmitter 214 and a receiver 216, each of which may includeone or more antennas that operate on one or more frequencies.

Wireless network nodes in telecommunication networks make periodicmeasurements of interference. The interference measurements can be usedto adapt network parameters to optimize performance in the presence ofinterference. However, it is important to characterize the interferencein order to implement appropriate optimization processes.

Causes of highly localized non-network interference include sparkingtransformers and industrial machinery. In addition to localizednon-network interference, cells experience interference fromtransmissions within the network itself.

Processes used to handle localized non-network interference aredifferent from processes used to handle interference from within thenetwork. Non-network interference is typically handled by locating andeliminating the interference source. For example, interference from asparking transformer can be mitigated by replacing the transformer.Network interference can be handled in a number of ways, includingadjusting network parameters such as power, frequency, antenna azimuthand beamwidth, and changing how transmissions are scheduled. In order toimplement the most effective processes for handling interference, it isimportant for networks to accurately detect and characterize theinterference.

Anomalous behavior in a wireless network is generally identified bycomparing measured values with values that are typical of themeasurements and flagging values that have large deviations from thetypical behavior. However, comparing sets of values with each otherrequires different techniques than techniques that are used forcomparing single values. In addition, due to the inherent instantaneousvariability of the wireless environment, it may be more effective tocompare statistics of measurements over the observation period ratherthan the sequence of observations over the observation interval.

FIG. 3 illustrates an embodiment of a process 300 for determining alocation of an external source of interference at a cell site. Theprocess may initiate when external interference is detected at S302.Wireless operators are licensed to use specific frequencies in the RFspectrum, and interference caused by normally scheduled transmissionswithin the operator's wireless network is internal interference. Incontrast, external interference is interference that is caused byequipment that is external to the operator's wireless network. There aremany possible sources for external interference, including unlicensed ormalfunctioning transmitters, industrial operations, sparkingtransformers, etc.

Techniques for detecting interference at cell sites, e.g. Signal toInterference plus Noise Ratio (SINR) measurements, are known in the artand can be applied at this step. Interference measurements that arecollected and stored by the network as PM data do not typically separatenetwork interference from external interference. Various techniques canbe applied for differentiating network interference from externalinterference as known in the art, such as correlating interferencemeasurements, and measuring interference during coordinated interferencetimes as described in U.S. Pat. No. 9,572,055.

Normally, interference measurement data stored in network elementsincludes not only the external interference signal, but also trafficinterference coming from mobile users connected to neighbor cell sitesas well. Hence, processes may be performed to separate externalinterference magnitude from the combined measured interference data ateach network element. For purposes of the present disclosure, thetechnique employed at S302 characterizes external interferencesufficiently to determine that the interference is external to thewireless network and strength of the interference at a plurality ofnetwork nodes.

After external interference is determined to be present in a networkarea, a set of cells of interest 402 is selected at S304. In anembodiment, the cells of interest are cells that are affected by theinterference, and more specifically cells at which external interferenceis detected. For example, when coordinated quiet times for specificfrequencies are used to determine that external interference is present,the set of cells may include every cell that detects a signal above thenoise floor in the specific frequencies at the quiet times.

After cells of interest are determined at S304, a grid pattern 404 isestablished around the cells of interest at S306. The grid patternestablishes a pattern of points around the cells of interest. FIG. 4illustrates an embodiment of a grid pattern 404 that is establishedaround the set 402 of cells of interest.

The size and density of the grid points may vary between embodiments.The grid is to establishes a set of sectors, or pixels, for which aprobability that an interference source being present is calculated.Because interference is evaluated for each pixel in a grid, a lower griddensity minimizes the number of calculations performed, while a highergrid density increases the resolution of a result.

The grid should be large enough to encompass the actual location of aninterference source. It is unlikely that an interference source islocated near unaffected cells, so the extent of a grid may be determinedbased on an area that encompasses all of the cells of interest 402. Insome embodiments, grid coordinates, e.g. geographic locationscorresponding to a grid, can be set and adjusted by a user. For example,if a user selects a first grid size that does not have any pixels withhigh probability values, the user could create a larger grid. Otherembodiments may automatically generate a grid, e.g. by establishing ageographic area that larger than affected cells by a predeterminedvalue. Although FIG. 4 shows grid 404 using cartesian coordinate lines,other shapes are possible.

A probability for the of a source of interference being located at eachpixel in a grid is determined at S308. FIG. 5 illustrates a Bayesianprocess 500 for determining probabilities for the source of interferencebeing located at each pixel.

Power values for receivers that detect interference are determined atS502. Power values may be determined for every antenna that has detecteda level of interference above the noise floor. The interferencemagnitudes from all cells of interest may be expressed as:P _(R) ={[P ₁ ¹ ,P ₂ ¹ , . . . P _(L) ₁ ¹],[P ₁ ² ,P ₂ ² , . . . ,P _(L)₂ ²], . . . ,[P ₁ ^(N) ,P ₂ ^(N) , . . . ,P _(L) _(N) ^(N)]},where P_(j) ^(i) is the measured interference magnitude at i-th site andj-th sector cell, L_(i) is the number of sector cells at the i-th siteand N is the total number of affected cell sites. In an embodiment, onlycell sites that have two or more sector cells are analyzed.

For each cell site, interference magnitude differences may be calculatedand stored as follows:ΔP _(R)={[ΔP ₁ ¹ ,ΔP ₂ ¹ , . . . ,ΔP _(L) ₁ ¹],[ΔP ₁ ² ,ΔP ₂ ² , . . .,ΔP _(L) ₂ ²], . . . ,[ΔP ₁ ^(N) ,ΔP ₂ ^(N) , . . . ,ΔP _(L) _(N)^(N)]}={[ΔP ₁ ¹ −ΔP ₂ ¹ ,P ₂ ¹ −P ₃ ¹ , . . . ,P _(L) ₁ ¹ −P ₁ ¹], . . .,[ΔP ₁ ^(N) −ΔP ₂ ^(N) ,P ₂ ^(N) −P ₃ ^(N) , . . . ,P _(L) _(N) ^(N) −P₁ ^(N)]}

Hypothetical interference magnitude values are determined at S504. In anembodiment, hypothetical interference magnitude values are determinedfor every cell of interest and every pixel in a grid 404. A hypotheticalinterference magnitude may reflect expected interference measurements ata cell site if an interference source exists at a given pixel.

When a site receives interference from an external source, the measuredinterference power depends on several link gain components, such aspathloss, transmit power, and antenna gain. For example, receiverinterference power can be expressed as follows:P _(R) =P _(T) +G _(T) +G _(R) −L _(T) −L _(R) −PL,where,P_(T) is an external interferer's transmit power in dB,G_(T) is transmit antenna gain at the interferer in dB,G_(R) is receiver antenna gain at the cells in dB,L_(T) is signal loss at the transmitter, e.g., cable loss, in dB,L_(R) is signal loss at the receiver, e.g., cable loss, in dB, andPL is pathloss from the radio channel, including shadowing, in dB.

Typically, no information is available for characteristics P_(T), G_(T),and L_(T) of a source of external interference. On the other hand,receiver information G_(R), L_(R) is available to an operator, andpathloss values can be estimated with a relatively high degree ofaccuracy, especially in consideration of known base station andgeographic characteristics.

For a given site, receiver signal power at two different cells can beexpressed as:P _(R1) =P _(T) +G _(T) +G _(R1) −L _(T) −L _(R1) −PL ₁,P _(R2) =P _(T) +G _(T) +G _(R2) −L _(T) −L _(R1) −PL ₂.

Considering the difference, P_(R1)−P_(R2), transmitter details arecanceled out as follows:ΔP _(R) =P _(R1) −P _(R2)=(G _(R1) −G _(R2))−(L _(R1) −L _(R2))−(PL ₁−PL ₂)In general, cable losses at different base stations can be assumed to bethe same without substantially compromising location accuracy. Hence,the ΔP_(R) can be expressed as:ΔP _(R)=(G _(R1) −G _(R2))−(PL ₁ −PL ₂)When the two cells are in the same site, the last term, (PL₁−PL₂), isalso canceled out because pathloss and shadowing values are consideredto be the same for cells of the same cell site.ΔP _(R)=(G _(R1) −G _(R2))

Accordingly, receiver power deltas may be calculated for specific pairsof cells. More specifically, receiver power deltas may be determined foreach pair of cellular antennas for a given cell site. When a cell siteis a three-sector site and external interference is detected at everysector of the cell site, hypothetical receiver power values arecalculated for all three combinations of antenna pairs. However, in anembodiment in which a cell site provides service to more than threecells, receiver gains need not be calculated for non-adjacent antennapairs. In one embodiment, only adjacent pairs of antennas are evaluatedfor a cell site.

FIG. 6A illustrates a three-sector cell site 600, and angles 602 a, 602b and 602 c, which are respective angles between pointing directions ofantennas of the cell site and the location of a pixel 604. The receiverantenna gain, G_(R), can be obtained by calculating the Angle of Arrival(AoA) 602 from the pixel to the receiver azimuth direction. In anembodiment, the AoA may be established to a center of each pixel.

After AoA values are obtained for each antenna, receiver antenna gainfor each antenna can be obtained using an antenna pattern. The antennapattern may be a generic pattern such as the pattern expressed by thefollowing formula:

$G_{R} = {- {\min\left( {{12 \times \left( \frac{AoA}{HPBW} \right)^{2}},{Am}} \right)}}$in which HPBW is half-power beam-width and Am is a minimum antenna gainlevel, for example, −25 dB. Alternatively, the antenna pattern may beprovided as a lookup table of gain values. For example, a lookup tablemay be created with a resolution of one value per degree for a total of360 values. In such an embodiment, the antenna gain for a given angle ofarrival can be obtained using the lookup table. In addition, anembodiment may apply interpolation for angles falling between the valueslisted in the table.

Once antenna gains at each cell from every pixel in a grid (k, k=1, . .. , M) are obtained, hypothetical data of relative interferencemagnitudes is determined (ΔH_(R)(k)), with an assumption that anexternal interference exists at a k-th pixel in the grid area, using theknown information for the data created from measurement data, ΔP_(R) asshown in the following equation:ΔH _(R)(k)={[ΔH ₁ ¹(k),ΔH ₂ ¹(k), . . . ,ΔH _(L) ₁ ¹(k)], . . . ,[ΔH ₁^(N)(k),ΔH ₂ ^(N)(k), . . . ,ΔH _(L) _(N) ^(N)(k)]}in which, for example, ΔH_(j) ^(i)(k)=G_(Ri,1)(k)−G_(Ri,2)(k), is thehypothetical receiver power difference between sector 1 and sector 2 atthe i-th site and L_(i) is the number of cells at the i-th site, i=1, 2,. . . , N. Thus, hypothetical values are established at S504 for pairsof antennas for each cell site.

When one or more measured interference level is equal to the noise floorlevel and the sector index that gives highest hypothetical receiverantenna gain matches with the highest measured interference magnitudesector, the hypothetical interference differences between the highestinterfered sector and those sectors with noise floor level ofinterference may be adjusted to have the same magnitude differences asmeasured magnitude. In this way, a localization algorithm can identify arange of angles where interference is arriving even when some ofmeasurement data are submerged by the noise floor. An example of this isshown in FIG. 6B.

FIG. 6B illustrates an embodiment of determining hypothetical antennagain values when noise-floor saturation is present. In that embodiment,a hypothetical antenna gain value of 0 dB is determined for Cell 3 basedon pixel location 604, and lower values, e.g. −10 dB and −20 dB aredetermined for cells 1 and 2. However, actual measured data from cells 1and 2 is limited by the noise floor of −120 dBm. In such asituation—where measured power is limited by the noisefloor—hypothetical antenna gain values of the cell site 600 are adjustedbased on the measured data. Resulting hypothetical gain values for thecell site in FIG. 6 would therefore be 0 dB for Cell 3, and −3 dB forCell 1 and Cell 2. Accordingly, an embodiment may adjust hypotheticalantenna gain values when those values fall below the noise floor, sothat cell sites with measurements below the noise floor can be used toimprove the results of localization.

When conventional techniques are applied, and an antenna's measurementsare limited by the noise floor, that antenna is effectively a nullitythat cannot be used to determine location. Thus, when two antennas of athree-sector cell site do not detect a signal above the noise floor, thecell site cannot contribute to localization using conventionaltechniques that employ angular data based on multiple antennas of a cellsite. In contrast, as described above, embodiments of the presentdisclosure can use measurements from every antenna that registers asignal to locate interference, even when one or more of the measurementsfrom co-sited antenna are limited by the noise floor.

Embodiments of the present disclosure apply a Bayesian approach toprobability by determining probabilities for a plurality of hypotheses,where each pixel location represents a hypothesis that an interferencesource is located at that pixel location. After having measured dataΔP_(R), and hypothetically created data for each pixel k, ΔH_(R)(k),where k=1, . . . , M, M being the total number of pixels in the gridarea. With a set of measurement data, P(=ΔP_(R)) and hypothetical data,H(k)(=ΔH_(R)(k)), the probability of external interference beingexisting at the k-th pixel can be expressed as:prob(k)=prob(H(k)|P)∝prob(P|H(k))·prob(H(k))∝prob(P|H(k)).The expression above uses proportionality instead of equality and alsoomits the denominator prob(P) and prob(H(k)) because they are constants.

The probability of interference existing at each pixel may be determinedusing measurement data and hypothetical data. The hypotheticalinterference values can be compared to measured values at S508 todetermine the likelihood that an interference source is present at agiven pixel, where closer matches between hypothetical data and measureddata suggest higher probabilities that an interferer is present at anassociated pixel. Since the equations described above remove unknownfactors such as interference transmit power and pathloss amount, thepixel whose H(k) matches with P will give probability of ‘1’ in theory.However, there are still a number of factors that lead to differencesbetween the measurement and hypothetical data, such as errors inmeasurement, differences in antenna models, etc.

Hence, inferring the location of interference may be achieved byreallocating the probability at each pixel (k) per site (i) as follows.

${{prob}\left( P \middle| {H(k)} \right)} = {\prod\limits_{i = 1}^{N}\;{{prob}\left( {{\Delta\;{\overset{\_}{P}}^{i}},{\Delta\;{{\overset{\_}{H}}^{i}(k)}}} \right)}}$where N is the total number of affected sites with two or more sectorcells, ΔP ^(i)=[P₁ ^(i)−P₂ ^(i), . . . , P_(L) _(i) ^(i)−P₁ ^(i)], andΔH ^(i)(k)=[ΔH₁ ^(i)(k), ΔH₂ ^(i)(k), . . . , ΔH_(L) _(i) ^(i)(k)] foran i-th site, where L_(i) is the number of sector cells at the i-thsite.

A probability, prob(ΔP ^(i), ΔH ^(i)(k)), can be obtained for each pixelusing a hypothesis that differences between ΔP ^(i) and ΔH ^(i)(k) atthe i-th site are caused by the randomness of radio environment andmeasurement devices even though interference actually exists at thepixel, k. As for the randomness of radio environment and measurementdevices, embodiments assume a normal distribution with a standarddeviation of σ.

The probability created from each site, prob(ΔP ^(i), ΔH ^(i)(k)) can beexpressed as:

${{prob}\left( {{\Delta\;{\overset{\_}{P}}^{i}},{\Delta\;{{\overset{\_}{H}}^{i}(k)}}} \right)} = {\prod\limits_{l = 1}^{L_{i}}\;{\int_{x = {{\Delta\; P_{l}^{i}} - {\Delta\;{H_{l}^{i}{(k)}}}}}{\frac{1}{\sigma\sqrt{2\pi}}{\exp\left\lbrack {- \frac{x}{2\sigma^{2}}} \right\rbrack}{dx}}}}$in which L_(i) is the number of sector cells at the site i, and L_(i)≥2.Since the distribution function is continuous, the probability of xexactly matching with ΔP_(l) ^(i)−ΔH_(l) ^(i)(k) will be zero.

In an embodiment, probability values may be applied from a limited setof values, or bins, based on a number of standard deviations betweenmeasured and hypothetical values at S508. As explained above, thestandard deviation here theoretically represents randomness in a numberof variables. However, the specific value of the standard deviation isnot the result of a calculation—rather, a value can be assigned to thestandard deviation by a user. Persons of skill in the art will recognizethat it is possible to assign various values to the standard deviationto affect the results of process 500.

A probability distribution function can be used to establish aprobability for ΔP_(l) ^(i)−ΔH_(l) ^(i)(k) falling within a particularrange of values, as opposed to taking on any one specific value. FIG. 7illustrates a normal curve that is divided into 9 separate bins.Embodiments are not limited by this specific example—in otherembodiments, different divisions are possible. In an embodimentaccording to FIG. 7, when the value of ΔP_(l) ^(i)−ΔH_(l) ^(i)(k) fallswithin a centered standard deviation [−σ/2, π/2], the probability willbe 38.2925%. Therefore, when the hypothetical interference value for apixel is plus or minus one half standard deviation from the measuredvalue, a probability value of 0.382925 is assigned to that pixel.Accordingly, higher probability values are assigned to pixels when thedifference between measured values and hypothetical values is lower, andvice versa.

After probability values have been determined at each pixel for eachpair of antennas with measurements above the noise floor, theprobability values for antenna pairs at each cell site are combined atS510. In an embodiment, the probability values are combinedmultiplicatively so that pixel values for each cell site are the productof all valid antenna pair probabilities for the cell site. In someembodiments, the probability values may be scaled to adjust thedifference between the highest and lowest probability values.

Probability values for all cell sites are combined at S512. FIGS. 8A-8Cillustrate an example of re-assigning probability values for each pixelafter calculating joint probability site by site. In FIGS. 8A-8C, darkcolored squares represent probability of external interference being atthe associated location, or pixel. The probability values are scaled tothe shade of the squares, so that squares representing pixels with ahigher probability have a darker shade than squares that represent lowerprobabilities. FIG. 8A illustrates probability values for all the pixelsin the grid are obtained from data at cell site 1 resulting fromcombining antenna pair probability values at S510.

FIG. 8B illustrates probability values that have been re-assigned aftercalculating joint probabilities with data for site 2. In other words,FIG. 8B represents a combination of probabilities for site 1 and site 2.Finally, FIG. 8C shows the final probability values for each pixel inthe grid using combined probability data from cell sites 1, 2 and 3.

In another embodiment, determining the probability of an interferer ateach pixel at S308 may be performed by determining a Euclidian distancebetween measured and hypothetical values. FIG. 10 illustrates anembodiment of a process 1000 of determining probability values forpixels in a grid using Euclidian distance.

In process 1000, determining measured antenna gain for each antennal atS1002 and determining hypothetical interference values at S1004 may beperformed using the same measurement data ΔP_(R) and hypotheticallycreated data for each pixel k, ΔH_(R)(k) from S502 and S504.Probabilities of an interference source being located at each pixel in agrid can be found based on Euclidean distances between measurement dataand hypothetical data at S1006. For example, Euclidian distance for eachpixel can be calculated as:

${{E\_ dist}{\_ k}} = {\sum\limits_{i = 1}^{N}{\sum\limits_{l = 1}^{L_{i}}\left( {{\Delta\; P_{l}^{i}} - {\Delta\;{H_{l}^{i}(k)}}} \right)^{2}}}$In this equation, the hypothetical interference magnitude vectors arecompared directly to measured interference vectors, and the differencesbetween the vectors are characterized as the Euclidian distance betweenthe vectors. In addition, determining differences between signalstrength measurement data and the hypothetical values to determine therespective probability values at S1006 may include raising thedifferences to some power. In the example shown in the above equation,the power is two, but other powers are possible. After calculatingdistance values E_dist_k for each pixel for each antenna pair, thedistance values for all pixels for each pair of antennas may be combinedat S1008, and the distance values for each cell site are combined atS1010. Combining the distance values may include, for example,determining respective products of distance values for each pixel.

Because smaller Euclidian distance values represent a higher probabilityof a source of interference being located at a given pixel, the valuesresulting from S1010 are inversely proportional to the actualprobability values. In other words, a smaller combined distance valuefor a pixel represents a higher probability of an interference sourcebeing located at that pixel. Accordingly, an additional step ofinversing and normalizing the distance values may be performed at S1012so that the scale of distances more closely matches the scale ofprobabilities represented by the distances.

The resulting probability values from process 1000 may be used togenerate a probability heat map at S310. In an embodiment, the heat mapis generated using values which are inversed and normalized at S1012. Inother words, a process for creating a graphical representation ofprobabilities for each pixel on a grid involves making the pixel thatyields smallest difference values between actual measurements andhypothetical data as the highest probable location of externalinterference. In an embodiment, inversing the differences to determineprobability values is generally represented by the following equation:

${{prob}(k)} = {\frac{1}{{E\_ dist}{\_ k}}/{\max\left( \frac{1}{{E\_ dist}{\_ k}} \right)}}$

Other embodiments of determining probabilities using Euclidian distanceare possible. Even though the distance values from S1006 are inverse toa conventional probability scale, it is possible to create a heat mapthat is useful to a wireless operator by applying an inverse graphicalscale, and/or using a non-linear graphical scale. Additional operations,e.g. logarithmic scaling, may be performed on the combined site valuesto provide a useful graphical or numeric output, such as the heat mapsshown in FIG. 11 and FIG. 12.

In the process 500 described above, each pixel is assigned a singleprobability value. However, depending on how the grid is established atS306, each pixel may represent a significant geographical area.Therefore, in an embodiment, a process 500 of determining probabilitycan be implemented that accounts for a range of probability levels thatcould be encompassed by the area represented by a single pixel.

FIG. 9A shows a process 900 of determining a range of an angle ofarrival for a pixel that may be performed as part of step S504 ofdetermining hypothetical interference values. As seen in FIG. 9B, pixelshapes 912 are circumscribed with circles 914 at S902. Although theshapes shown in FIG. 9B are squares, other shapes are possible, such ashexagons or triangles.

When the Angle of Arrival to a cell is ϕ, the range of a hypotheticalAoA for a pixel can be expressed as [ϕ−θ,ϕ+θ], where

$\theta = {\frac{\pi}{2} - {{\cos^{- 1}\left( \frac{R}{d} \right)}.}}$As seen in FIG. 9C, θ is the angle between line 916 that runs between alocation of cell site 918 and the center point 920 of a pixel, and line922 that is tangential to circle 914 and runs through the location ofcell site 918. Here, ΔH_(l) ^(i)(k) may be obtained with thesehypothetical AoA ranges, and maximum and minimum values of (ΔP_(l)^(i)−ΔH_(l) ^(i)(k)) may be used as a range to determine a probabilityfrom the probability distribution function.

Ranges of ΔP_(l) ^(i)−ΔH_(l) ^(i)(k) values, e.g. determining (ΔP_(l)^(i)−ΔH_(l) ^(i)(k))_(min) and (ΔP_(l) ^(i)−ΔH_(l) ^(i)(k))_(max) foreach pixel may be determined at S904. In an embodiment that accounts forrange, ranges may be applied to assign probability values at S508 byapplying the calculated range directly to the following equation:

${{prob}\left( {{\Delta\;{\overset{\_}{P}}^{i}},{\Delta\;{{\overset{\_}{H}}^{i}(k)}}} \right)} = {\prod\limits_{l = 1}^{L_{i}}{\int{\int_{{({{\Delta\; P_{l}^{i}} - {\Delta\;{H_{l}^{i}{(k)}}}})}_{\min}}^{{({{\Delta\; P_{l}^{i}} - {\Delta\;{H_{l}^{i}{(k)}}}})}_{\max}}{\frac{1}{\sigma\sqrt{2\pi}}{\exp\left\lbrack {- \frac{x}{2\sigma^{2}}} \right\rbrack}{dx}}}}}$Pixels are represented as squares 902, and circles 904 are circumscribedaround each square pixel. The circumscribed circle 914 presents auniform size (diameter) from every possible location on the grid ofpixels, so that angular calculations are not affected by the unevenprofile of a square.

AoA ranges for pixels diminish in proportion to distance from a cellsite. Therefore, an effect of accounting for pixel range is that closerpixels have a larger AoA ranges, resulting in higher probability valuesthan pixels that are farther from a cell site. Diminishing probabilityaccording to distance may reflect variability in a radio environment,where larger distances have a higher probability of being occupied byobjects or terrain that affects the radio environment.

In an embodiment, a probability heat map is generated at S310. FIG. 11shows an embodiment of a heat map 1100 that is generated by anembodiment of the present disclosure. In the heat map 1100 in FIG. 11,the pixels of the grid are shaded according to a probability of a sourceof external interference 1004 being present at each pixel location. Theshading of the heat map is scaled so that darker areas represent ahigher probability of the interference source being present, and lighterareas indicate lower probability values.

Two external interference sources are present in FIG. 11, labeled as1004 a and 1004 b. The locations of the interference sources 1004 aremarked with an “X.” FIG. 11 illustrates two distinct interferencesources as indicated by the two distinct associated dark colored pixelclusters. Heat map 1100 in FIG. 11 was generated using simulatedmeasurement data for a plurality of cell sites 1102.

In FIG. 11, probability values were scaled according to a logarithmicscale to show a relatively broad probability distribution. In contrast,FIG. 12 shows a heat map 1100 that is scaled to show local maxima 1106and a relatively minor variance of probability values for surroundingpixels. FIG. 12 was created using the same data as FIG. 11, except thatprobability values used to shade pixels are not log values. Pixels 1106a and 1106 b with the highest probability values are very near tointerference locations 1104 a and 1104 b, respectively, and maximumpixel value 1106 b is geographically coincident with interference source1106 b. Accordingly, the locations in FIG. 12 are highly accurate.

FIG. 13 represents a result of performing a conventional trilaterationprocess to determine a location 1006 of the interference source 1004 a.The conventional process was able to identify a location 1006 thatcorresponds to one source of external interference with a reasonabledegree of accuracy, but was unable to identify the second source 1004 b.The ability to resolve and identify multiple sources of interference ina single geographic area is a substantial advantage of embodiments ofthe present disclosure compared to conventional technologies, whichtypically yield a single point in space as an interference location.

The heat map of FIG. 13 was generated using the same data as FIG. 11 andFIG. 12. The conventional process resulted in identifying aninterference location 1106 two pixels away from the location ofinterference source 1104 a. In contrast, the Experimental example ofFIG. 12 according to an embodiment of a Bayesian process according tothe present disclosure indicates a maximum probability of interferencesource 1104 a being less than one pixel away at pixel 1106 a. The higheraccuracy apparent by comparing FIG. 12 to FIG. 13 is another advantageof embodiments of the present disclosure compared to conventionalinterference location techniques.

A location for a source of interference is determined at S312. FIG. 12represents an example of identifying a source of interference, where thepixels representing probability maxima 1106 may be provided as locationswith the highest probability of interference being present. In anotherembodiment, a heat map with a broader distribution of probabilities asrepresented by FIG. 11 may be provided to identify interference sourcelocations. Persons of skill in the art will recognize that variations ina radio environment may affect measurement accuracy, so a limiteddistribution of probability values as represented in FIG. 12 may notaccurately identify a source of interference. Accordingly, embodimentsmay use various scales of a probability heat map to identify one or moreexternal source of interference.

In an embodiment, identifying an external source of interference at S312may involve providing a heat map of probability values to techniciansthat deploy in the field with RF signal detection equipment to pinpointthe specific physical location and cause of interference. In otherembodiments, network personnel may use a heat map or other form ofprobability distribution data in conjunction with geographic informationto identify a source of interference without deploying personnel in adrive test. For example, probability maxima may coincide with a locationof an entity that is known to cause interference, such as a televisiontransmitter or radar. An embodiment may automate interference sourceidentification by correlating high probability values and knownlocations for potential sources of interference.

Process 300 may further characterize the interference with, for example,time and frequency information, that can help a network operator rapidlyand efficiently identify its source. For example, time, amplitude andfrequency characteristics can be used to determine that interference isfrom a predetermined source, such as a sparking electrical coupling or aradar installation.

The external interference may be resolved at S314. In an embodiment,resolving the source of external interference may include interfacingwith the source of interference to ensure that it no longer transmits infrequencies licensed to a wireless operator. In another embodiment,resolving interference at S314 may include adapting parameters of awireless communications system to avoid or minimize the impact of asource of external interference.

Although aspects of process 1000 rely on information from multi-sectorcell sites that use directional antennas, cell sites withomnidirectional antennas can also be useful for identifying a source ofinterference. Processes using omnidirectional antennas to locate aninterference source may be used in conjunction with the processesdisclosed above to obtain more accurate results than would be availableusing multi-sector cell sites or omnidirectional cell sites alone.

FIG. 14 shows an embodiment of a process 1400 that uses data fromomnidirectional antennas to locate a source of interference. Process1400 may initiate when external interference is detected at S1402. Whenexternal interference is detected, a pixel map may be created for ageographic region affected by the interference at S1404. Both of theseelements—S1402 and S1404—may be performed in accordance with process300. Detecting external interference at S1402 may be performed bydetecting external interference at S302, and creating a pixel map may beperformed in accordance with one or more of elements S304 to S310. In anembodiment, an output of element S1404 is a probability heat map asdescribed with respect to S310 above.

A system determines whether data from cell sites using omnidirectionalantennas is performed at S1406. When data from omnidirectional antennasis available to the system performing process 1400, the system maycompare the interference data from the omnidirectional antennas to theinterference data from the multi-sector cell sites to determine whetherthe same interference is affecting both types of antennas at S1408.Comparing the interference data may include comparing one or morecharacteristic of the interference, such as frequency or timecharacteristics of the interference signals, etc.

The system may create a contour map using data from the omnidirectionalcell sites at S1410. An example of such a process is explained in U.S.Pat. No. 9,942,775, which is incorporated herein by reference, and willnow be explained with respect to a process 1500 of creating a signalstrength contour map shown in FIG. 15.

FIG. 16 shows an embodiment of locations of and interference values atomnidirectional cell sites OS that may be identified at S1502. Whilemost of the sites OS register interference measurements at the noisefloor of −120 dB, three of the sites—OS6, OS7 and OS8—have measuredlevels of interference power that are significantly above the noisefloor. Accordingly, data from these cell sites can be useful foridentifying the location of the measured interference source.

The omnidirectional sites OS may be connected to one another usingDelaunay triangulation at S1504, as seen in FIG. 17. After the sites areconnected, a number of contour lines may be determined at S1506. Highernumbers of contour lines increase resolution of the data, but alsogenerally require more resources to generate and analyze.

In the example of affected cells in FIG. 3, the highest interferencepower is −98.6977 dBm. If 5 contour lines are desired, the difference ofhighest interference and lowest interference (−120 dBm) is divided by 6and the resulting interference levels for contour lines are: {−116.4496,−112.8992, −109.3488, −105.7985, −102.2481}. Each of the contour linesmay be described as a perimeter line that represents a particular levelof interference and defines a bounded shape within the line.

Locations for the contour values along lines of the Delaunay trianglesmay be determined by interpolating signal values between the nodes onboth sides of the lines. The interpretation may be linear interpolation,but embodiments are not limited to that technique. The interferencepoints are then connected to other points with the same value at S1510to generate a map with contour lines 1802 as seen in FIG. 18.

FIG. 19 shows an embodiment where limited omnidirectional antenna datais available for a geographic region that is affected by externalinterference. In the example of FIG. 19, while a sufficient number ofomnidirectional cell sites are available on the left side of the figureto establish a set of contour lines, there are no omnidirectional siteson the right side of site OS7, so the contour lines are open ended onthe right side of the figure. In these circumstances, closed contourlines can still be established by introducing a virtual cell site atS1512.

A virtual site may be provided at S1512 by adding one or more verticesin the direction of the open end. The approximate distance to a virtualsite along a vertex can be obtained using a pathloss model. In theexample shown in FIG. 19, assuming the interference source is on theextended line of two affected cells (OS8 and OS7) toward the open area,the distance to a virtual site, where the expected interference levelwould be noise-floor level, can be obtained according to the followingexample. The receiver interference power can be written as:P _(R) =P _(T) +G _(T) +G _(R) −L _(T) −L _(R) PL(d),in which:P_(T) is an external interferer's transmit power in dB,G_(T) is a transmit antenna gain at the interferer in dB,G_(R) is receiver antenna gain at the cells in dB,L_(T) is signal loss at the transmitter, e.g. cable loss, in dB,L_(R) is signal loss at the receiver, e.g. cable loss, in dB, andPL(d) is pathloss from the radio channel, including shadowing, in dB,where d is the distance between two locations.

Considering that “P_(T)+G_(T)+G_(R)−L_(T)−L_(R)” would likely be thesame for all omnidirectional sites, the following equalities may beassumed:P _(R_OS7) +PL(D1)=P _(R_OS8) +PL(D1+D2)=P _(R-virtual-site) +PL(D3),With respect to the contour plot in FIG. 20, X is a possible location ofan interference source 2004, D1 is distance in meters between X and OS7,D2 is distance in meters between OS7 and OS8, and D3 is distance inmeters between X and a virtual site. Calculating values for the Exampleof FIG. 20 yieldsP_(R_OS7)=−99.9583 dBm,P_(R_OS8)=−102.5742 dBm, andP_(R_virtual-site)=−120.0 dBm.

Using the Hata pathloss model, PL(d)=128.1−37.6·log 10(d/1000), whered=distance in meters, the distance from OS7 to virtual site is obtainedas 1.3984 km. Other embodiments may use different pathloss models. Afterthe location of the virtual site is established, interpolation isperformed to establish locations of the contour values on each Delaunaytriangle line per S1508, and contour lines are generated at S1510 tocreate a contour map such as the map shown in FIG. 20.

Returning to process 1400 and FIG. 14, after a contour map usingomnidirectional cell sites has been created at S1410, theomnidirectional site data is combined with multi-sector cell site dataat S1412. In an embodiment, the data is combined by re-assigning theprobability values of a pixel map for multi-sector antennas, e.g. aprobability heat map from S310, using data from an omnidirectional sitecontour map.

For example, combining data at 1412 can be accomplished by re-assigningprobability by weighting the original probability values as follows:

(k)=Pr(k)·Pr _(cont)(k),where

${\Pr_{cont}(k)} = \left\{ \begin{matrix}p_{1} & {{if}\mspace{14mu}{pixel}\mspace{14mu} k\mspace{14mu}{is}\mspace{14mu}{inside}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu} 1{st}\mspace{14mu}{contour}\mspace{14mu}{level}} \\p_{2} & {{if}\mspace{14mu}{pixel}\mspace{14mu} k\mspace{14mu}{is}\mspace{14mu}{inside}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu} 2{nd}\mspace{14mu}{contour}\mspace{14mu}{level}\mspace{14mu}{and}\mspace{14mu}{outside}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu} 1{st}\mspace{14mu}{contour}\mspace{14mu}{level}} \\\ldots & \ldots\end{matrix} \right.$

The weights may be established based on the number of affected indoorcells or level of interference. For example, one possible set of weightsis p₁=1, p₂=0.5, p₃, . . . , p_(N+1)=0, where N is the number ofdifferent contour levels, all the pixels within the highest contour willhave a weight of 1 and all the pixels within the second highest contourbut outside of the highest contour have a weight of 0.5, and all otherpixels will have weight of 0. Other embodiments of contour weights arepossible—for example, in some embodiments every contour has a weightthat is greater than zero.

After applying weights, the re-assigned probability

(k) may be normalized so that the sum of probabilities across all pixelsis 1.

An example of re-assigning probability at S1412 using a tiered weightingscheme for contour levels is shown in FIGS. 21A-C. FIG. 21A shows aprobability heat map that was generated using data from multi-sectorcell sites using process 300. The resulting probability distribution isan unbounded conical shape that extends to the right from cell site2102. The probability distribution of FIG. 21A can occur by performingprocess 300 when the number of cell sites affected by interference islow. Although the source of interference 2104 is within the highprobability zone, the size of the high probability zone is relativelylarge, so it may be difficult to positively locate an interferer usingthe probability map of FIG. 21A.

FIG. 21 B shows a contour map that was created using data fromomnidirectional antennas 2106 according to the processes discussedabove. When the contour map of FIG. 21B is combined with the probabilitymap of FIG. 21A, the resulting probability distribution is shown in FIG.21C. In FIG. 21C, the contour map of FIG. 21B has been applied to theprobability distribution from FIG. 21A by multiplying the innermostcontour by 1, multiplying the second contour by 0.5, and multiplyingpixels of all other contours—including pixels outside of the outermostcontour—by zero. The resulting distribution is bounded to a limitedarea, and is a considerably more accurate correlation to the actuallocation of the source of interference 2104 than the originalprobability distribution of FIG. 21A.

FIGS. 22A-C illustrate another example of combining a pixel-basedprobability map from multi-sector cell site data with a contour mapgenerated from omnidirectional antenna data. The pixel map in FIG. 22Ashows two discrete open-ended distributions emanating from multi-sectorcell site 2202 in two distinct directions, and represents a possibleoutcome from performing process 300. FIG. 22B represents a contour mapthat was generated using data from omnidirectional antennas 2202measuring interference from the same source 2204 as FIG. 22A.

When probabilities of the pixel map of FIG. 22A are modified using thecontours of FIG. 22B, the utility of the probability distribution isdramatically improved. As seen in FIG. 22C, one of the two open-endeddistributions is eliminated entirely, and the shaded geographic areaindicating higher probability is much more focused on the actuallocation of interference source 2204.

Although the embodiments shown in FIGS. 21A and 22A show unboundedprobability distributions, it is possible to perform S1410 and S1412 toa variety of localizations. For example, contour maps generated fromomnidirectional cell site data can be applied by reassigning the pixelprobability distributions of FIGS. 11 to 13, or to other kinds ofinterference maps. Accordingly, data from omnidirectional antennas canbe combined with data from multi-sector antennas to identify a locationof a source of interference with a higher degree of accuracy than datafrom omnidirectional or multi-sector antennas alone.

When omnidirectional antenna data is not available at S1406, process1400 may improve the accuracy of a probability map using cellularcoverage areas. An example of this will be explained with respect toFIGS. 23A-C.

FIG. 23A shows a probability pixel map generated using process 300 withan unbounded single probability distribution shape extending frommulti-sector cell site 2202 that is similar to the unbounded probabilitydistribution shown in FIG. 21A. However, unlike the embodiment shown inFIG. 21A, no data from omnidirectional antennas is available within themap grid of FIG. 23A.

FIG. 23B shows a result of estimating coverage area for cell sites atS1414. In the embodiment of FIG. 23B, coverage areas for the cellularantennas of multi-sector cell sites 2202 are represented by Voronoipolygons. An example of creating Voronoi polygons to represent cellcoverage can be found, for example, in U.S. application Ser. No.15/076,539. However, embodiments are not limited to the Voronoi polygonsshown in FIG. 23B—in other embodiments, cell coverage areas could berepresented by other shapes, including curved shapes and shapes thatoverlap one another. In an embodiment, cell coverage areas from aplanning tool or pre-existing database could be used.

The cell coverage data of FIG. 23B is combined with the probability mapof FIG. 23A at S1416 to identify the location of interference source2204. In the embodiment of FIG. 23C, the data is combined by attenuatingprobability values from FIG. 23A according to the coverage shapes ofFIG. 23B. The re-assigned probability can be expressed as:

(k)=Pr(k)·Pr _(cov)(k),where

${\Pr_{cov}(k)} = \left\{ \begin{matrix}p_{1} & {{{if}\mspace{14mu}{the}\mspace{14mu}{pixel}\mspace{14mu} k\mspace{14mu}{is}\mspace{14mu}{inside}\mspace{14mu}{of}\mspace{14mu}{voronoi}\mspace{14mu}{polygon}\mspace{14mu}{of}\mspace{14mu}{an}\mspace{14mu}{affected}\mspace{14mu}{cell}},} \\p_{2} & {{{if}\mspace{14mu}{the}\mspace{14mu}{pixel}\mspace{14mu} k\mspace{14mu}{is}\mspace{14mu}{inside}\mspace{14mu}{of}\mspace{14mu}{voronoi}\mspace{14mu}{polygon}\mspace{14mu}{of}\mspace{14mu}{adjacent}\mspace{14mu}{cell}},} \\p_{3} & {{otherwise}.}\end{matrix} \right.$

may be normalized so that the sum of probabilities across all pixels is1.

In the example of FIG. 23C, values for p₁-p₃ are {p₁=1, p₂=0.5, p₃=0.5}.However, these values are merely an example, and other values arepossible in other embodiments. For example, embodiments may use coverageareas that are ranked according to the level of interference measured bythe cell. In other embodiments, e.g. embodiments with high accuracycoverage areas, antennas that do not detect interference may be excludedby applying a weighting of zero.

Although the examples of FIGS. 23A-C show data using multi-sector cells,omnidirectional antennas can be used at S1414. In such an embodiment,coverage area of an omnidirectional cell site may be represented as acircle centered at the site, and the size of the circle may be scaledbased on the coverage area of the omnidirectional cell. The interferencedetected by the omnidirectional antenna may be correlated with theinterference detected by the multi-sector cell before re-assigningprobabilities of the probability map.

Finally, interference may be resolved at S1418, for example by providinga physical output that identifies one or more probable location for aninterference source. The map may be a cartesian map indicatinggeographic coordinates such as latitude and longitude, and may have anelement that identifies a cardinal direction, such as true north. Themap may be output by printing ink onto paper, or displayed on one ormore electronic screen. An electronic version of data associated withthe map may be stored in a computer memory, and that data may betransmitted to individuals or organizations involved in interferencedetection and localization in wireless telecommunication networks.

In other embodiments, resolving interference at S1418 may be performedin accordance with the explanation of resolving interference at S314described above.

Embodiments of the present disclosure represent numerous improvements tointerference detection technology. As seen with respect to FIGS. 11-13,embodiments can identify multiple discrete sources of interferencewithin a predetermined geographical area. In addition, testing performedby the inventors has established that embodiments of the presentdisclosure are more accurate than conventional techniques used in thewireless industry. One reason for improved accuracy is that embodimentscan use measurement data from a cell site even in a case where only oneantenna measures interference levels above the noise floor. In addition,embodiments can provide a range of probability values across ageographic area.

Conventional approaches suffer from many shortcomings. Trilateration andtriangulation have much lower accuracy, and generally yield a singlepoint in space. Conventional approaches cannot typically identifylocations of multiple sources of interference. When multiple sources ofinterference are present, location results of conventional localizationtechniques used in the wireless industry are incapable of identifyingtwo separate sources, and the accuracy of identifying the location of asingle interference source may be substantially compromised.

An operator can use information from embodiments of this disclosure todeploy personnel to remedy the physical cause of interference, such asshutting down a rogue transmitter or repairing a sparking transformer.An operator may be a licensor of RF spectrum that operates a cellulartelecommunications network. Furthermore, embodiments of the presentdisclosure can analyze and characterize interference without requiringnetwork service interruptions, and without installing additional energysensing equipment in network areas.

Embodiments relate to a method that detects and locates externalinterference source using network management data without having tomeasure and collect additional interference data using separatemeasurement devices. Based on network management data, such as CM(Configuration Management), PM (Performance Management) and Topologydata, an embodiment creates hypothetical data for external interferenceand generates a probability heat map of possible locations of externalinterference source around the affected area. Embodiments may beimplemented without details of transmitted interference signals, anddiminish the impact of fading and shadowing in a radio environment, sothat a location of external interference can be identified with a highdegree of accuracy.

What is claimed is:
 1. A method for locating a source of interferenceaffecting a wireless telecommunications network, the method comprising:generating a first probability map that indicates probabilities of thesource of interference being disposed in respective geographiclocations; receiving interference data for at least one omnidirectionalantenna that is disposed in a location covered by the map; generating acontour map of interference levels using the received interference data;and generating a second probability map by re-assigning probabilityvalues of the first probability map according to contours of the contourmap, wherein generating the contour map includes generating a pluralityof bounded shapes, each shape having a perimeter line that represents aparticular level of interference.
 2. The method of claim 1, wherein thefirst probability map includes an unbounded probability area originatingfrom a directional antenna location.
 3. The method of claim 2, whereinre-assigning the probability values of the first map includes boundingthe unbounded probability area.
 4. The method of claim 2, wherein thefirst probability map includes a second unbounded probability area, andre-assigning the probability values includes reducing probability valuesof the second unbounded probability area to zero.
 5. The method of claim1, wherein the first probability map comprises a plurality of pixels,and each pixel has an assigned probability value that represents aprobability that the source of interference is located in a geographicarea corresponding to the pixel.
 6. The method of claim 1, furthercomprising: comparing characteristics of interference used to generatethe first probability map to characteristics of the receivedinterference data from the at least one omnidirectional antenna todetermine whether the interference used to generate the firstprobability map has the same characteristics as interference associatedwith the received interference data.
 7. The method of claim 1, furthercomprising: assigning an attenuation value to each bounded shape,wherein generating the second probability map includes re-assigningprobability values of the first map according to the attenuation valuesof the bounded shapes.
 8. The method of claim 1, wherein generating thecontour map includes performing Delaunay triangulation to connectlocations for the omnidirectional antennas, and the contours includecontour lines between sides of the Delaunay triangles.
 9. The method ofclaim 8, wherein at least one of the locations for the omnidirectionalantennas used to generate the contour map is a virtual cell site atwhich no physical cell site exists.
 10. The method of claim 1, whereinthe interference is external interference that is not caused by thewireless telecommunications network.
 11. The method of claim 10, furthercomprising: resolving the external interference.
 12. A wirelesstelecommunications system comprising a spectrum analysis serverconfigured to locate a source of interference external to a wirelesstelecommunications network, the spectrum analysis server comprising aprocessor and a non-transitory computer readable medium withinstructions stored thereon which, when executed by the processor,perform the following steps: generating a first probability map thatindicates probabilities of the source of interference being disposed inrespective geographic locations; receiving interference data for atleast one omnidirectional antenna that is disposed in a location coveredby the map; generating a contour map of interference levels using thereceived interference data; and generating a second probability map byre-assigning probability values of the first probability map accordingto contours of the contour map, wherein generating the contour mapincludes generating a plurality of bounded shapes, each shape having aperimeter line that represents a particular level of interference. 13.The system of claim 12, wherein the first probability map includes anunbounded probability area originating from a directional antennalocation.
 14. The system of claim 13, wherein re-assigning theprobability values of the first map includes bounding the unboundedprobability area.
 15. The system of claim 13, wherein the firstprobability map includes a second unbounded probability area, andre-assigning the probability values includes reducing probability valuesof the second unbounded probability area to zero.
 16. The system ofclaim 12, wherein the first probability map comprises a plurality ofpixels, and each pixel has an assigned probability value that representsa probability that the source of interference is located in a geographicarea corresponding to the pixel.
 17. The system of claim 12, wherein theinterference data is received from a performance management server ofthe wireless telecommunications network that is coupled to the at leastone omnidirectional antenna.
 18. The system of claim 12, wherein thesteps executed by the processor further comprise: assigning anattenuation value to each bounded shape, wherein generating the secondprobability map includes re-assigning probability values of the firstmap according to the attenuation values of the bounded shapes.
 19. Amethod for locating a source of interference affecting a wirelesstelecommunications network, the method comprising: generating a firstprobability map that indicates probabilities of the source ofinterference being disposed in respective geographic locations;receiving interference data for at least one omnidirectional antennathat is disposed in a location covered by the map; generating a contourmap of interference levels using the received interference data; andgenerating a second probability map by re-assigning probability valuesof the first probability map according to contours of the contour map,wherein the first probability map comprises a plurality of pixels, andeach pixel has an assigned probability value that represents aprobability that the source of interference is located in a geographicarea corresponding to the pixel.